Výsledky vyhledávání - "Fathy, Mohammed E."

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TDT: Teaching Detectors to Track without Fully Annotated Videos

Autor: Yu, Shuzhi, Wu, Guanhang, Gu, Chunhui, Fathy, Mohammed E.

Recently, one-stage trackers that use a joint model to predict both detections and appearance embeddings in one forward pass received much attention and achieved state-of-the-art results on the Multi-Object Tracking (MOT) benchmarks. However, their s

Externí odkaz: http://arxiv.org/abs/2205.05583

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Hierarchical Metric Learning and Matching for 2D and 3D Geometric Correspondences

Autor: Fathy, Mohammed E., Tran, Quoc-Huy, Zia, M. Zeeshan, Vernaza, Paul, Chandraker, Manmohan

Interest point descriptors have fueled progress on almost every problem in computer vision. Recent advances in deep neural networks have enabled task-specific learned descriptors that outperform hand-crafted descriptors on many problems. We demonstra

Externí odkaz: http://arxiv.org/abs/1803.07231

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Fundamental Matrix Estimation: A Study of Error Criteria

Autor: Fathy, Mohammed E., Hussein, Ashraf S., Tolba, Mohammed F.

The fundamental matrix (FM) describes the geometric relations that exist between two images of the same scene. Different error criteria are used for estimating FMs from an input set of correspondences. In this paper, the accuracy and efficiency aspec

Externí odkaz: http://arxiv.org/abs/1706.07886

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Sparse Representations and Feature Learning for Image Set Classification and Correspondence Estimation

Autor: Fathy, Mohammed E.

The use of effective features is a key component in solving many computer vision tasks including, but not limited to, image (set) classification and correspondence estimation. Many research directions have focused on finding good features for the tas

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::8e270307957ea10554f4fbf8b493be32

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